Dialysis attendance patterns and health care utilisation of Aboriginal patients attending dialysis services in urban, rural and remote locations

Background Aboriginal people in the Northern Territory (NT) suffer the heaviest burden of kidney failure in Australia with most living in remote areas at time of dialysis commencement. As there are few dialysis services in remote areas, many Aboriginal people are required to relocate often permanently, to access treatment. Missing dialysis treatments is not uncommon amongst Aboriginal patients but the relationship between location of dialysis service and dialysis attendance (and subsequent hospital use) has not been explored to date. Aim To examine the relationships between location of dialysis service, dialysis attendance patterns and downstream health service use (overnight hospital admissions, emergency department presentations) among Aboriginal patients in the NT. Methods Using linked hospital and dialysis registry datasets we analysed health service activity for 896 Aboriginal maintenance dialysis patients in the NT between 2008 and 2014. Multivariate linear regression and negative binomial regression analyses explored the associations between dialysis location, dialysis attendance and health service use. Results We found missing two or more dialysis treatments per month was more likely for Aboriginal people attending urban services and this was associated with a two-fold increase in the rate of hospital admissions and more than three-fold increase in ED presentations. However, we found higher dialysis attendance and lower health service utilisation for those receiving care in rural and remote settings. When adjusted for age, time on dialysis, region, comorbidities and residence pre-treatment, among Aboriginal people from remote areas, those dialysing in remote areas had lower rates of hospitalisations (IRR 0.56; P < 0.001) when compared to those who relocated and dialysed in urban areas. Conclusion There is a clear relationship between the provision and uptake of dialysis services in urban, rural and remote areas in the NT and subsequent broader health service utilisation. Our study suggests that the low dialysis attendance associated with relocation and care in urban models for Aboriginal people can potentially be ameliorated by access to rural and remote models and this warrants a rethinking of service delivery policy. If providers are to deliver effective and equitable services, the full range of intended and unintended consequences of a dialysis location should be incorporated into planning decisions. Supplementary Information The online version contains supplementary material available at 10.1186/s12913-022-07628-9.


pg. 2 Electronic Supplementary Material Methods and Results
This supplement includes additional information relating to the methods and results for the dialysis attendance and health care utilisation analysis including cohort definition and data management.

Setting
Most of the Northern Territory in Australia is classified as remote and very remote according to the Australian Statistical Geography Standard (1) which classifies areas according to degrees of remoteness. Only Darwin is identified as Outer Regional. For the purposes of this study and ease of differentiation between services, we classified treatment locations into three categories (urban, rural and remote) based on access to health services and the Australian Governments classification of hospitals (2): • Urban: Darwin and Alice Springs serviced by Principal Referral or Acute Group A Hospitals.
• Rural: Katherine and Tennant Creek serviced by small Acute Group C Hospitals • Remote: all other treatment centres without hospitals.

Methods
Australia's health system is a mixture of publicly and privately funded services. Dialysis treatments in the Northern Territory are fully publicly funded under a case mix model based on coded discharge hospital data. All haemodialysis treatments, including those conducted in satellite facilities, are entered into the NT Department of Health's (DoH) Admitted Patient Care (APC) (hospital) dataset. There is only one private hospital in the NT, which does not provide dialysis treatments. Therefore the capture of dialysis activity in the NT is considered to be comprehensive and robust.
The full database population for this study was derived from the DoH's APC (hospital) dataset combined with the Australia and New Zealand Dialysis Transplant Registry (ANZDATA) dataset.
The APC hospital dataset contains individual episodes of patient care for the five (public) parent hospitals and several satellite services in the NT, from the beginning of consistent electronic record keeping (1991). It includes demographic details of the individual (age, ethnicity, residence) and the hospital (hospital code, ward/s), as well as admission/separation codes and diagnosis and procedure codes (primary and up to 49 secondary codes) based on the International Classification of Diseases version 10, Australian Modification (ICD 10AM). ANZDATA is the data repository for people receiving maintenance kidney replacement therapy (KRT) in Australia and New Zealand and contains patient level administrative and clinical data, based on an annual census from participating renal units. All renal units in the NT participate in the census.
pg. 3 The full database population included: 1) any individual from the APC dataset with an ICD 10AM diagnosis or procedure code for dialysis or transplantation (Table S1) between the years 2000 and 2015 (n= 2844); and 2) any individual from the ANZDATA dataset who registered as ever having dialysis in the NT between 2000 and 2015 (n=1390).  The two datasets were linked by a third-party jurisdictional data linkage agency (SA/NT Datalink) following standard ethical systems and protocols. SA/NT Datalink is an independent agency based at the University of South Australia. Using probabilistic matching, de-identified individuals across data sets were linked and assigned a unique identifier. Due to the voluntary nature of the ANZDATA collection, a one to one (1:1) match with the hospital dataset was not expected and one hundred and thirty-two (132) individuals in the hospital dataset were not present in the ANZDATA set. Sixty-seven (67) individuals in ANZDATA dataset did not match any individuals in the hospital data set. These 67 individuals were excluded as hospital activity data was not available for analysis. The datasets included all hospital admission and registry data for eligible patients.
The full database population of 2844 individuals was then linked with activity data from two additional data sets: a) interstate patient travel information (n=171 patients); and b) dialysis data from individuals (n=189) receiving care in the community controlled DxMoC4 and self-care HD DxMoC5. This was necessary because inconsistencies in data entry for these models led to some gaps in attendance data, however, manual compilation of activity between 2008-2014 was possible and linkage with the hospital data set was undertaken by an independent linker not associated with the project.

Study cohort definition
The final study population (n=896) included individuals who had any KRT for more than three months continuously (to eliminate acute and short term dialysis support including patients visiting from elsewhere on holidays), between the years 2008 to 2014. This date range was chosen as some models of care only became fully established after 2008 and the additional activity data (for DxMoC4 and DxMoC5) was provided to the end of 2014. Restricting the analysis of patterns of health service utilisation to 2008 to 2014 ensured that sufficiently robust activity data across all models was available for the analysis.
Patients were excluded if they were 16 years and younger at 2008 or did not have at least one admission after 2008 -to exclude patients who left the NT and were therefore not eligible for inclusion. Non-Aboriginal patients (n=107) were also excluded as they comprised less than 10% of the population and did not experience all models of care. All available admission data pre 2008 was also retained for the purposes of identifying home residence and health status (comorbidities) prior to commencing dialysis.  Each admission was aligned with a KRT treatment option of haemodialysis (HD), peritoneal dialysis (PD) or transplant based on diagnosis or procedure codes (Table S3). Relevant diagnosis and procedure codes were present for all patients for at least one admission a year but not all admissions contained a KRT relevant code. Table S3: ICD 10AM diagnostic and procedure codes used to establish dialysis model of care To determine whether an individual relocated for treatment and whether any treatment was received at or closer to home, we identified home address (to suburb level) for each admission episode that occurred in the 24 months prior to commencement of KRT, taking the earliest admission address as the residence pre-KRT start. Less than 1% of patients did not have an admission in the 24 months prior to KRT start and their home address was taken as the address when they started KRT.
Patients were categorized as 'Relocated' when they lived outside the urban areas of Darwin and Alice Springs prior to commencement of KRT. All patients start KRT in the urban areas of Darwin and Alice Springs regardless of original residence. Limited capacity in rural and remote areas mean patients from these areas are not guaranteed treatment in or near their community and thus are placed on urban government housing priority lists once in the urban area. Hostel accommodation is usually available in the interim if not staying with family, although many patients never leave hostel accommodation due to the very long housing wait list. We defined 'Relocation' as having to change residence from rural/remote to urban, indefinitely, in order to access KRT.
Remoteness of a patient's home address was determined by mapping their residence (suburb or community) pre-KRT start to the Modified Monash Model (MMM), which classifies metropolitan, regional, rural and remote areas according to seven levels of geographical remoteness (3 MMM categories apply to the NT: MMM2 Outer regional (which we renamed 'Urban' for the purposes of this study); MMM6: Remote and MMM7: Very remote.
We used the MMM for home residence as the Australian Government recently approved a Medicare Benefits Schedule (MBS) item (4), for staffed dialysis in very remote locations (MMM7). We were interested in understanding how many patients might possibly benefit from this item.
Activity was separated by region, Top End (TE) and Central Australia (CA), to align with health service responsibility in the NT.
Variable definitions (descriptions and calculations) are shown in Table S4. pg. 8     Dialysis attendance, hospital admissions and ED presentations Low dialysis attendance is associated with increased rates of hospitalisations and ED presentations as shown in Table S7.

ED Presentations
When examining ED presentations, the unadjusted analysis showed increased rates associated with male gender (IRR=1.18, 95% CI:1.05-1.32) compared to female, remoteness of residence pre-KRT start, time on dialysis greater than 12 months, and the comorbidities of diabetes, vascular disease, cardiac disease and obesity (Table S8). However, on multivariate analysis, time on dialysis was not significant and the only comorbidity that remained significant was cardiac disease with an IRR=1.44; 95% CI: 1.26-1.64. Remoteness of residence pre-KRT start was associated with increased rates of ED presentations, while the rates were substantially lower for rural, remote and self-care models (DxMoC2-6) ( Table S8).
This suggests that relocated people receiving care in the urban area had higher rates of ED presentations compared to people who were able to return to their communities and receive care at or closer to home in DxMoC2-6. However, we acknowledge that people in remote areas have little chance of presenting to a local emergency department, and if requiring care will be medically transferred to the urban hospital. Our data set did not include information about emergency medical transfers. However, if a medical transfer was warranted, it would result in an admission to hospital and as shown in Table 5 in the manuscript, the rates of hospital admissions for remote models was lower than urban models. We do not believe there has been a significant underestimation of ED use by patients attending rural and remote models.
pg. 15 Table S8: ED presentation incident rate ratios (IRR) for NT Aboriginal patients, 2008Aboriginal patients, -2014 Days in hospital We also examined the total annual days in hospital (Table S9). Most exposure variables tested individually were statistically significant. The mean annual days were higher for those with a comorbidity of diabetes, cardiac disease, vascular disease and obesity compared to those without the respective comorbidity, while dialysing in remote and self-care HD models (DxMoC3 to 5) was associated with lower mean annual days in hospital compared to Incentre DxMoC0. When modelled together the associations persisted although the difference in days in hospital for those with and without obesity were fewer. Self-care PD DxMoC6 had relatively high adjusted mean annual days in hospital at 24.1 (95% CI:22.9-25.4) compared to Incentre DxMoC0 of 18.3 (95% CI:17.5-19.0) days in hospital (Table S9) An analysis of diagnosis codes for this group noted a higher rate of admissions for more severe and complex conditions when compared to the other models of care (5).